William V. Stoecker

Automatic detection of asymmetry in skin tumors

Asymmetry, a critical feature in the diagnosis of malignant melanoma, is analyzed using a new algorithm to find a major axis of asymmetry and calculate the degree of asymmetry of the tumor outline. The algorithm provides a new objective definition of asymmetry. A dermatologist classified 86 tumors as symmetric or asymmetric. Borders of tumors were found either manually or automatically using a radial search method. With either method, asymmetry determination by the asymmetry algorithm agreed with the dermatologists determination of asymmetry in about 93% of cases.

Automatic detection of irregular borders in melanoma and other skin tumors

An irregularity index previously developed is applied to detect irregular borders automatically in skin tumor images, particularly malignant melanoma. The irregularity index is used to classify various tumor borders as irregular or regular. This procedure processes tumor images with borders automatically determined by a radial search algorithm previously described. Potential use of this algorithm in an in vivo skin cancer detection system and errors expected in the use of the algorithm are discussed.

An automatic color segmentation algorithm with application to identification of skin tumor borders

A principal components transform algorithm for automatic color segmentation of images is described. This color segmentation algorithm was used to find tumor borders in six different color spaces including the original red, green, and blue (RGB) color space of the digitized image, the intensity/hue/saturation (IHS) transform, the spherical transform, chromaticity coordinates, the CIE transform and the uniform color transform designated CIE-LUV. Five hundred skin tumor images were separated into a training set and a test set for comparison of the different color spaces. Automatic induction was applied to dynamically determine the number of colors for segmentation. Ninety-one percent of image variance was contained in the image component along the principal axis (also containing the most image information). When compared to a luminance radial search method, the principal components color segmentation border method performed equally well by one measure and 10% better by another measure, including more near border points outside the tumor. The spherical transform provides the highest success rate and the chromaticity transform the lowest error rate, although large variances in the data preclude definitive statistical comparisons.

Boundary detection in skin tumor images: an overall approach and radial search algorithm

Abstract Although computerized boundary detection has been studied in depth, current general algorithms are not highly successful when applied to in vivo medical images where the borders are often not clearly defined and are sometimes difficult even for the human eye to detect. In these cases, domain-specific algorithms are necessary to achieve the required accuracy. This paper addresses the problem of automatic detection of tumor borders in digitized images of skin tumors. The complexity of boundary detection in this domain is such that algorithms based on single boundary determinants such as color, luminance, texture, or three-dimensional information are not capable of correctly identifying the boundary in all cases. Hence, an overall approach is discussed that will use confidence levels to combine results from several border detection algorithms based on the individual criteria mentioned. The development of one such algorithm (using a radial search method on luminance information) and its results are presented. With this approach, problems such as numerous false borders and unknown shape and size of the tumors are overcome.

Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

Background: Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non‐invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area).

Editorial: digital imaging in dermatology

Abstract In this article we discuss the recent surge in activity in digital imaging in dermatology. The key role of digital imaging as an adjunct to detection of early malignant melanoma, with application in following patients with the dysplastic nevus syndrome, is explored. Other current and future uses of digital imaging in image archiving, in clinical studies such as hair growth studies, and in telediagnosis are reviewed. We review the varying research activities of image analysis laboratories participating in the dermatology image researching group. Research laboratories included in this group are at Oregon Health Sciences University, Xerox Corporation, University of Arizona, University of Cincinnati, University of Munich, University of Wurzburg, University of Arkansas, Harvard University, Southern Illinois University-Edwardsville, Johns Hopkins University, National Institutes of Health, and University of Missouri at Columbia and Rolla. The role of new imaging devices in dermatology including the “nevoscope” and the dermatoscope is explored. Goals and challenges for the new technology are discussed.

Detection of atypical texture features in early malignant melanoma

Background: The presence of an atypical (irregular) pigment network (APN) can indicate a diagnosis of melanoma. This study sought to analyze the APN with texture measures.

Nondermatoscopic digital imaging of pigmented lesions

Background/aims: Pigmented lesions are often difficult to evaluate clinically. Improvement of diagnostic accuracy by dermatoscopy has attracted much interet. With advanced digital imaging measurement of assymmetry, border irregularity and relative color as well as texture characteristics, lesional depth and changes in lesional area are now possible, the object of this review is to conclude the present status of these techniques and their potential.

Detection of solid pigment in dermatoscopy images using texture analysis.

Background/aims: Epiluminescence microscopy (ELM), also known as dermoscopy or dermatoscopy, is a non‐invasive, in vivo technique, that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such feature is the solid pigment, also called the blotchy pigment or dark structureless area. Our goal was to automatically detect this feature and determine whether its presence is useful in distinguishing benign from malignant pigmented lesions.

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.